zipformer BF16 training recipe

egs/librispeech/ASR/RESULTS.md

@@ -307,6 +307,23 @@ done
 
 To decode with external language models, please refer to the documentation [here](https://k2-fsa.github.io/icefall/decoding-with-langugage-models/index.html).
 
+We also support training Zipformer with AMP+bf16 format (requires bf16 support). See [here](https://github.com/k2-fsa/icefall/pull/1700) for more details and pre-trained models. **The same command can be used for decoding and exporting the model.**
+
+The amp+bf16 training command is:
+```bash
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
+./zipformer/train.py \
+  --world-size 4 \
+  --num-epochs 50 \
+  --start-epoch 1 \
+  --use-fp16 0 \
+  --use-bf16 1 \
+  --exp-dir zipformer/exp_amp_bf16 \
+  --causal 0 \
+  --full-libri 1 \
+  --max-duration 1000
+```
+
 ##### small-scaled model, number of model parameters: 23285615, i.e., 23.3 M
 
 The tensorboard log can be found at

egs/librispeech/ASR/zipformer/scaling.py

@@ -297,7 +297,7 @@ def forward(ctx, x: Tensor, dim: int):
         # (presumably) that op does not support float16, and autocast
         # is enabled.
         if torch.is_autocast_enabled():
-            ans = ans.to(torch.float16)
+            ans = ans.to(torch.get_autocast_gpu_dtype())
         ctx.save_for_backward(ans)
         ctx.x_dtype = x.dtype
         ctx.dim = dim
@@ -1234,7 +1234,7 @@ class DoubleSwishFunction(torch.autograd.Function):
     @staticmethod
     def forward(ctx, x: Tensor) -> Tensor:
         requires_grad = x.requires_grad
-        if x.dtype == torch.float16:
+        if x.dtype == torch.float16 or x.dtype == torch.bfloat16:
             x = x.to(torch.float32)
 
         s = torch.sigmoid(x - 1.0)
@@ -1346,7 +1346,7 @@ class SwooshLFunction(torch.autograd.Function):
     @staticmethod
     def forward(ctx, x: Tensor) -> Tensor:
         requires_grad = x.requires_grad
-        if x.dtype == torch.float16:
+        if x.dtype == torch.float16 or x.dtype == torch.bfloat16:
             x = x.to(torch.float32)
 
         zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
@@ -1379,7 +1379,7 @@ def forward(ctx, x: Tensor) -> Tensor:
                 d_int = d_scaled.to(torch.uint8)
                 ctx.save_for_backward(d_int)
                 if x.dtype == torch.float16 or torch.is_autocast_enabled():
-                    y = y.to(torch.float16)
+                    y = y.to(torch.get_autocast_gpu_dtype())
                 return y
 
     @staticmethod
@@ -1425,7 +1425,7 @@ class SwooshRFunction(torch.autograd.Function):
     def forward(ctx, x: Tensor) -> Tensor:
         requires_grad = x.requires_grad
 
-        if x.dtype == torch.float16:
+        if x.dtype == torch.float16 or x.dtype == torch.bfloat16:
             x = x.to(torch.float32)
 
         zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
@@ -1455,7 +1455,7 @@ def forward(ctx, x: Tensor) -> Tensor:
                 d_int = d_scaled.to(torch.uint8)
                 ctx.save_for_backward(d_int)
                 if x.dtype == torch.float16 or torch.is_autocast_enabled():
-                    y = y.to(torch.float16)
+                    y = y.to(torch.get_autocast_gpu_dtype())
                 return y
 
     @staticmethod

egs/librispeech/ASR/zipformer/train.py

@@ -521,6 +521,13 @@ def get_parser():
         help="Whether to use half precision training.",
     )
 
+    parser.add_argument(
+        "--use-bf16",
+        type=str2bool,
+        default=False,
+        help="Whether to use bf16 in AMP.",
+    )
+
     add_model_arguments(parser)
 
     return parser
@@ -1027,7 +1034,9 @@ def save_bad_model(suffix: str = ""):
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch.cuda.amp.autocast(
+                enabled=params.use_autocast, dtype=params.dtype
+            ):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1047,9 +1056,7 @@ def save_bad_model(suffix: str = ""):
             scaler.update()
             optimizer.zero_grad()
         except Exception as e:
-            logging.info(
-                f"Caught exception: {e}."
-            )
+            logging.info(f"Caught exception: {e}.")
             save_bad_model()
             display_and_save_batch(batch, params=params, sp=sp)
             raise
@@ -1090,7 +1097,7 @@ def save_bad_model(suffix: str = ""):
                 rank=rank,
             )
 
-        if batch_idx % 100 == 0 and params.use_fp16:
+        if batch_idx % 100 == 0 and params.use_autocast:
             # If the grad scale was less than 1, try increasing it.    The _growth_interval
             # of the grad scaler is configurable, but we can't configure it to have different
             # behavior depending on the current grad scale.
@@ -1109,14 +1116,14 @@ def save_bad_model(suffix: str = ""):
 
         if batch_idx % params.log_interval == 0:
             cur_lr = max(scheduler.get_last_lr())
-            cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0
+            cur_grad_scale = scaler._scale.item() if params.use_autocast else 1.0
 
             logging.info(
                 f"Epoch {params.cur_epoch}, "
                 f"batch {batch_idx}, loss[{loss_info}], "
                 f"tot_loss[{tot_loss}], batch size: {batch_size}, "
                 f"lr: {cur_lr:.2e}, "
-                + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "")
+                + (f"grad_scale: {scaler._scale.item()}" if params.use_autocast else "")
             )
 
             if tb_writer is not None:
@@ -1128,7 +1135,7 @@ def save_bad_model(suffix: str = ""):
                     tb_writer, "train/current_", params.batch_idx_train
                 )
                 tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
-                if params.use_fp16:
+                if params.use_autocast:
                     tb_writer.add_scalar(
                         "train/grad_scale", cur_grad_scale, params.batch_idx_train
                     )
@@ -1204,9 +1211,25 @@ def run(rank, world_size, args):
             params.ctc_loss_scale = 1.0
         else:
             assert params.ctc_loss_scale + params.attention_decoder_loss_scale == 1.0, (
-                params.ctc_loss_scale, params.attention_decoder_loss_scale
+                params.ctc_loss_scale,
+                params.attention_decoder_loss_scale,
             )
 
+    if params.use_bf16:  # amp + bf16
+        assert torch.cuda.is_bf16_supported(), "Your GPU does not support bf16!"
+        assert not params.use_fp16, "You can only use either fp16 or bf16"
+        params.dtype = torch.bfloat16
+        params.use_autocast = True
+    elif params.use_fp16:  # amp + fp16
+        params.dtype = torch.float16
+        params.use_autocast = True
+    else:  # fp32
+        params.dtype = torch.float32
+        params.use_autocast = False
+
+    logging.info(f"Using dtype={params.dtype}")
+    logging.info(f"Use AMP={params.use_autocast}")
+
     logging.info(params)
 
     logging.info("About to create model")
@@ -1339,7 +1362,7 @@ def remove_short_and_long_utt(c: Cut):
             params=params,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    scaler = GradScaler(enabled=params.use_autocast, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1439,7 +1462,9 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch.cuda.amp.autocast(
+                enabled=params.use_autocast, dtype=params.dtype
+            ):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,